The present invention relates to medical image based detection of coronary stenoses, and more particularly, to automatic detection and classification of coronary stenoses in cardiac computed tomography (CT) volumes.
According to the American Heart Association, coronary artery disease (CAD) is one of the leading causes of death in the western world. Every year, approximately six million patients in United States emergency departments are evaluated for acute chest pain. The current standard for diagnosis is the conventional invasive coronary angiography, which is expensive and involves a high amount of risk. New generations of high-performance CT scanners, and in particular the advent of dual-source CT scanners, have enabled the acquisition of high-quality Coronary CT Angiography (CCTA) images. A multitude of clinical studies have proven the utility of CCTA for detection of coronary lesions, and in particular for the evaluation of emergency room patients with acute chest pain using the so-called “triple rule-out” technique. Because of their high quality, CCTA images may be a viable alternative for invasive angiography in the near future. In particular, the high negative predicative value of CCTA images allows a physician to rule out aortic dissection, pulmonary embolism, and significant stenoses in the coronary vessels by a single CT examination. However, reading CCTA images requires substantial experience and only well-trained physician typically are able to interpret CCTA images appropriately.
The detection, classification, and rating of coronary stenoses in CCTA images is challenging due to varying image quality due to low signal-to-noise ratios and motion/reconstruction artifacts. Even experts may struggle to give a correct diagnosis using CCTA images. This may lead to incorrect or inconsistent evaluation of coronary stenoses. Accordingly, automatic detection of various types of stenoses in the coronary vessels is desirable.